kselect {adehabitat} | R Documentation |
Performs a multivariate analysis of ecological data (K-select analysis).
kselect(dudi, factor, weight, scannf = TRUE, nf = 2, ewa = FALSE) ## S3 method for class 'kselect': print(x, ...) ## S3 method for class 'kselect': kplot(object, xax = 1, yax = 2, csub = 2, possub = c("topleft", "bottomleft", "bottomright", "topright"), addval = TRUE, cpoint = 1, csize = 1, clegend = 2, ...) ## S3 method for class 'kselect': hist(x, xax = 1, mar=c(0.1,0.1,0.1,0.1), ncell=TRUE, csub=2, possub=c("bottomleft", "topleft", "bottomright", "topright"), ncla=15, ...) ## S3 method for class 'kselect': plot(x, xax = 1, yax = 2, ...)
dudi |
an object of class dudi |
factor |
a factor with the same length as nrow(dudi$tab) |
weight |
a numeric vector of integer values giving the weight
associated to the rows of dudi$tab |
scannf |
logical. Whether the eigenvalues bar plot should be displayed |
nf |
if scannf = FALSE , an integer indicating the number
of kept axes |
ewa |
logical. If TRUE , uniform weights are given
to all animals in the analysis. If FALSE , animal weights are
given by the proportion of relocations of each animal (i.e. an
animal with 10 relocations has a weight 10 times lower than an
animal with 100 relocations) |
x, object |
an object of class kselect |
xax |
the column number for the x-axis |
yax |
the column number for the y-axis |
addval |
logical. If TRUE , the frequency of the
relocations per animal is displayed (see examples) |
cpoint |
the size of the points (if 0, the points where no relocations are found are not displayed) |
mar |
the margin parameter (see help(par) ) |
ncell |
logical. If TRUE , the histogram shows the
distribution of the cells of
the raster map where at least one relocation is found. If
FALSE , the histogram shows the distribution of the
relocations |
csub |
the character size for the legend, used with
par("cex")*csub |
csize |
the size coefficient for the points |
clegend |
the character size for the legend used by
par("cex")*clegend |
possub |
a character string indicating the sub-title position
("topleft", "topright", "bottomleft", "bottomright") |
ncla |
the number of classes of the histograms |
... |
additional arguments to be passed to the generic function
histniche , print or, in the case of plot.kselect ,
s.distri |
The K-select analysis is intended for hindcasting studies of habitat
selection by animals using radio-tracking data. Each habitat variable
defines one dimension in the ecological space. For each animal, the
difference between the vector of average available habitat conditions
and the vector of average used conditions defines the marginality
vector. Its size is proportional to the importance of habitat
selection, and its direction indicates which variables are
selected. By performing a non-centered principal component analysis of
the table containing the coordinates of the marginality vectors of
each animal (row) on the habitat variables (column), the K-select
analysis returns a linear combination of habitat variables for which
the average marginality is greatest. It is a synthesis of variables
which contributes the most to the habitat selection. As with principal
component analysis, the biological significance of the factorial axes is
deduced from the loading of variables.
plot.kselect
returns a summary of the analysis: it displays (i)
a graph of the correlations between the principal axes of the PCA of
the objects of class dudi
passed as argument and the factorial
axes of the K-select analysis; (ii) a graph giving the scores of the
habitat variables on the factorial axes of the K-select analysis;
(iii) the barplot of the eigenvalues of the analysis (each eigenvalue
measure the mean marginality explained by the axis; (iv) the
projection of the non-recentred marginality vectors on the factorial
plane (the origin of the arrow indicates the average used habitat
conditions, and the end of the arrow indicates the average used
conditions); (v) the projection of the resource units available to
each animal on the first factorial plane and (vi) the coordinates of
the recentred marginality vectors (i.e. recentred so that they have a
common origin) on the first factorial plane.
kplot.kselect
returns one graph per animal showing the
projections of the available resource units on the factorial plane, as
well as their use by the animal. hist.kselect
does the same
thing, but on one dimension instead of two.
kselect
returns a list of the class kselect
and
dudi
(see dudi
).
Clement Calenge clement.calenge@oncfs.gouv.fr
Calenge, C., Dufour, A.B. and Maillard, D. (2005) K-select analysis: a new method to analyse habitat selection in radio-tracking studies. Ecological modelling, 186, 143–153.
sahrlocs2kselect
for
conversion of objects class sahrlocs
to objects suitable for a
K-select analysis, s.distri
, and
dudi
for class dudi
.
## Not run: ## Loads the data data(puechabon) sahr <- puechabon$sahr ## prepares the data for the kselect analysis x <- sahrlocs2kselect(sahr) tab <- x$tab ## Example of analysis with two variables: the slope and the elevation. ## Have a look at the use and availability of the two variables ## for the 4 animals tab <- tab[,((names(tab) == "Slope")|(names(tab) == "Elevation"))] tab <- scale(tab) tmp <- split.data.frame(tab, x$factor) wg <- split(x$weight, x$factor) opar <- par(mfrow = n2mfrow(nlevels(x$factor))) for (i in names(tmp)) s.distri(scale(tmp[[i]]), wg[[i]]) par(opar) ## We call a new graphic window x11() ## A K-select analysis acp <- dudi.pca(tab, scannf = FALSE, nf = 2) kn <- kselect(acp, x$factor, x$weight, scannf = FALSE, nf = 2) # use of the generic function scatter scatter(kn) # Displays the first factorial plane kplot(kn) kplot(kn, cellipse = 0, cpoint = 0) kplot(kn, addval = FALSE, cstar = 0) # this factorial plane can be compared with # the other graph to see the rotation proposed by # the analysis graphics.off() # Displays the first factorial axis hist(kn) # Displays the second factorial axis hist(kn, xax = 2) # Summary of the analysis plot(kn) ## End(Not run)